Operational photovoltaics power forecasting using seasonal time series ensemble

摘要

The hypothesis, namely, ensemble forecasts improve forecast accuracy, is herein investigated. More specifically, this paper considers an application of day-ahead operational PV power output forecasting using time series ensembles. Since numerical weather prediction (NWP) is strongly favored for day-ahead solar forecasting, the motivation of using seasonal time series forecasting models is made clear at the beginning of the paper. A total of 142 models from six families are considered: the SARIMA family of models (36 models), ETS family of models (30 models), MLP (1 model), STL decomposition (2 models), TBATS family of models (72 models) and the theta model (1 model), see main text for descriptions. These models first undergo a within-group competition judged using the Akaike information criterion (AIC). The forecasts made by the six winning models, one from each family, are then combined using eight different methods: (1) simple averaging, (2) variance-based combination, (3) combination through ordinary least squares regression, (4) least absolute deviation regression, (5) constrained least squares regression, (6) complete subset regressions, (7) AIC-weighted subset regressions, and (8) lasso regression. Methods (3) to (8) cover most aspects of regression-based forecast combinations.